Python Powerhouse Gen AI From Basics to Advanced Programming
What you'll learn
- Build robust web applications using Python and Flask
- Master Flask fundamentals: routing, templates, forms, and sessions.
- Design and implement RESTful APIs with Flask
- Master Flask fundamentals and advanced concepts
- Deploy Flask applications to production environments
- Deploy Flask applications to production environments.
Requirements
- Basic Python programming knowledge is recommended. No prior Flask experience is required.
- Basic Python programming knowledge
- Understanding of HTML, CSS, and JavaScript (recommended)
Description
Python Powerhouse Gen AI From Basics to Advanced Programming
Welcome to Master the Machine Muse: Python Programming for Generative Art & Design, a comprehensive course designed to blend the realms of art and technology through the power of Python programming. This course is meticulously crafted for those who wish to harness the capabilities of Python and generative AI to create stunning visual art and innovative design solutions.
In this course, you'll embark on a journey that starts with the fundamentals of Python and progresses through advanced techniques in generative art and design. Whether you're a beginner or an experienced programmer, this course provides a structured pathway to mastering Python’s essential features and applying them to the creative field of generative art.
Section 1: Lists and Tuples in Python
Begin your journey by understanding the foundational data structures in Python—lists and tuples. You'll explore how to create, manipulate, and utilize these structures effectively. Through practical exercises and case studies, you'll learn to perform operations such as sorting, indexing, and slicing lists, as well as understand the immutable nature of tuples. This section provides a solid grounding in Python's core data types, essential for any advanced programming tasks.
Section 2: Sets and Dictionaries in Python
Delve into more advanced data structures: sets and dictionaries. Learn to define and manipulate sets, handle various operations like union, intersection, and difference, and manage dictionaries with key-value pairs. This section equips you with the skills to handle more complex data scenarios and optimize your data management strategies.
Section 3: Date and Time Manipulations
Master the art of handling date and time data with Python. You'll learn how to format and manipulate date-time objects, extract meaningful components, and apply these skills in real-world data scenarios. This section includes a case study on customer churn prediction, where you’ll practice data preprocessing and feature engineering techniques crucial for accurate data analysis.
Section 4: Functional Programming
Explore functional programming paradigms with Python, focusing on lambda functions, map, reduce, and filter operations. Understand how to apply these concepts to simplify code and perform efficient data transformations. This section provides a deep dive into functional programming techniques, offering practical examples and applications.
Section 5: Advanced Python Programming
Expand your Python expertise with advanced programming concepts such as recursion, feature engineering, and data analysis. You'll tackle problems like handshake calculations using iterative and recursive methods, and learn to engineer date-time features for machine learning models. This section also includes an in-depth look at the IRIS dataset, enhancing your understanding of evaluation metrics like precision, recall, and AUC ROC.
Section 6: Python Libraries and Data Analysis
Gain proficiency in using Python’s standard libraries for mathematical operations, random number generation, and file handling. Explore exploratory data analysis techniques through practical examples, such as football play analysis, and understand how to manage and clean data effectively.
Section 7: Advanced Data Visualization Techniques
Learn advanced visualization techniques to interpret and present data insights. This section covers distribution plots, KDE plots, joint plots, and the identification of outliers. You’ll develop skills to create compelling visual representations of data, aiding in both analysis and storytelling.
Section 8: Model Building and Evaluation
Finally, apply your skills to build and evaluate machine learning models. Learn the process of data preprocessing, model training, and evaluation with a focus on logistic regression. Understand key evaluation metrics and how to interpret them to improve your model’s performance.
By the end of this course, you will have a robust understanding of Python programming and its application in generative art and design. You will be equipped with the skills to create innovative art, handle complex data, and apply advanced techniques to real-world problems. Whether you aim to pursue a career in data science, art, or design, this course will provide the foundational and advanced skills needed to excel in these fields.
Who this course is for:
- Python developers looking to expand into web development
- Aspiring web developers interested in using Python
- Individuals wanting to build web applications with a lightweight framework
- Anyone curious about creating interactive online experiences
- Aspiring web developers seeking a hands-on approach
- Students or professionals interested in building web applications
Instructor
- 4.0 Instructor Rating
- 1,142 Reviews
- 72,373 Students
- 45 Courses
Hello, I'm Akhil Vydyula — Lead Data Engineer at Publicis Sapient, and Former Senior Data Scientist at PwC.
With over 5 years of rich industry experience and a strong focus on the BFSI sector, I’ve led and delivered end-to-end data and analytics solutions that power strategic decisions and transform business outcomes.
At Publicis Sapient, I currently lead complex data engineering initiatives, leveraging my deep expertise in cloud-native platforms like AWS to architect robust, scalable data pipelines. My work spans across developing and optimizing ETL workflows using PySpark and Spark SQL, orchestrating data flows via EMR, Step Functions, and EventBridge, and driving real-time and batch data processing into PostgreSQL (RDS/Redshift) environments. I've also implemented AWS Glue and DMS to seamlessly replicate and transform large-scale on-premise data into cloud-native formats.
Previously, at PwC, I specialized in advanced analytics and machine learning within the Advisory Consulting practice. I’ve built and deployed predictive models using statistical analysis, regression, classification, clustering, and text mining—particularly for risk identification and decision modeling. My passion lies in transforming raw data into actionable insights through effective data storytelling and visualization.
In parallel to my corporate career, I bring over 5 years of teaching experience, mentoring hundreds of aspiring data professionals. I’m deeply committed to helping students break into the data industry by translating real-world challenges into practical learning experiences.
Whether it's building data pipelines, uncovering business insights, or shaping the next generation of data talent, I thrive at the intersection of technology, strategy, and impact.
Let’s connect if you're passionate about data, eager to learn, or looking to collaborate on meaningful, data-driven initiatives.